knitr::opts_chunk$set(warning = FALSE, message = FALSE)
library(RNifti)
library(ggplot2)
library(patchwork)
library(DiagrammeR)
For the test, I have 10 subjects with complete data. This is the exchangeability structure used:
graph <- readr::read_lines('ptree.dot')
graph <- c(graph[1], 'graph [layout = circo]', graph[2:length(graph)])
DiagrammeR::grViz(graph)
Each participant has 2 runs with 7 conditions in each run. The contrast of interest subtracts the average of 3 conditions from the average of the other 4 conditions.
I first created 100 permuted design matrices based on the scheme above, shuffling rows within-participant. I then ran PALM (see config below) using each of these permuted design matrices (using -n 1000 permutations).
/ContrastName1 Go-CR
/NumWaves 7
/NumContrasts 1
/PPheights 1.000571e+00
/RequiredEffect 0.632
/Matrix
2.500000e-01 2.500000e-01 2.500000e-01 2.500000e-01 -3.330000e-01 -3.330000e-01 -3.330000e-01
/NumWaves 7
/NumPoints 140
/PPheights 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
/Matrix
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Download ten_perfect_l3_con_perm_01_palmconfig.txt
# Configuration file for PALM.
# Version alpha118
, running in MATLAB 9.10.0.1602886 (R2021a).
# 27-Aug-2021 11:34:10
-i ../Go-CR.4d.dtseries.nii
-transposedata
-eb ../eb.csv
-d l3_contrast_perm_01.mat
-t ../l3_contrast.con
-n 1000
-within
-ee
-save1-p
-o ten_perfect_l3_con_perm_01
-savemetrics
-savemax
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First, I look at the uncorrected p-values fro the output files perms/ten_perfect_l3_con_perm_*_dat_tstat_uncp.dscalar.nii.
fns <- sprintf('perms/ten_perfect_l3_con_perm_%02d_dat_tstat_uncp.dscalar.nii', 1:100, 1:100)
d <- data.table::rbindlist(mapply(function(x, i){
if(file.exists(x)){
data.table::data.table(p = as.numeric(RNifti::readNifti(x)),
#p = 10^(-as.numeric(RNifti::readNifti(x))),
id = i)
} else {
data.table::data.table()
}
}, fns, 1:length(fns), SIMPLIFY = FALSE))
sprintf('Range of data values (1-p): [%0.3f, %0.3f]', range(d$p)[1], range(d$p)[2])
## [1] "Range of data values (1-p): [0.000, 0.999]"
sprintf('Number of analyses of unique permuted design mats: %d', length(unique(d$id)))
## [1] "Number of analyses of unique permuted design mats: 100"
Density plots for each of the 100 runs, and histograms for the data overall, and zoomed in to the left and right tails.
ggplot(d, aes(x = p)) +
# geom_histogram(binwidth = .05) +
geom_density() +
facet_wrap(~ id, nrow = 10) +
theme(strip.background = element_blank(),
strip.text.x = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()) +
ggplot(d, aes(x = p)) +
geom_histogram(binwidth = .02) +
ggplot(d[p>.9], aes(x = p)) +
geom_histogram(binwidth = .005) +
labs(title = 'p-1 > .90') +
ggplot(d[p<.1], aes(x = p)) +
geom_histogram(binwidth = .005) +
labs(title = 'p-1 < .10') +
plot_layout(design = "
AAB
AAC
AAD")
#these are 1-tailed tests, 1-p
ggplot(rbind(d[, .(id = 0,
prop_sig = sum(p>.95)/.N,
run = 'Overall')],
d[, .(prop_sig = sum(p>.95)/.N,
run = 'Individual'), by = 'id']),
aes(x = id, y = prop_sig, fill = run)) +
geom_col() +
theme(axis.text.x = element_blank()) +
labs(title = 'Error rate for 1-tailed test', x = '', y = 'Proportion voxels p < .05')
ggplot(rbind(d[, .(id = 0,
prop_sig = sum(p<.05)/.N,
run = 'Overall')],
d[, .(prop_sig = sum(p<.05)/.N,
run = 'Individual'), by = 'id']),
aes(x = id, y = prop_sig, fill = run)) +
geom_col() +
theme(axis.text.x = element_blank()) +
labs(title = 'Error rate for other side of 1-tailed test\n(Just in case)', x = '', y = 'Proportion voxels p < .05')
Now looking at the FWE-corrected output.
fns <- sprintf('perms/ten_perfect_l3_con_perm_%02d_dat_tstat_fwep.dscalar.nii', 1:100, 1:100)
dfwe <- data.table::rbindlist(mapply(function(x, i){
if(file.exists(x)){
data.table::data.table(p = as.numeric(RNifti::readNifti(x)),
#p = 10^(-as.numeric(RNifti::readNifti(x))),
id = i)
} else {
data.table::data.table()
}
}, fns, 1:length(fns), SIMPLIFY = FALSE))
sprintf('Range of data values (1-p): [%0.3f, %0.3f]', range(dfwe$p)[1], range(dfwe$p)[2])
## [1] "Range of data values (1-p): [0.000, 0.963]"
Number of runs with at least one significant voxel: 1
Proportion of runs with at least one significant voxel: 0.01